Rib suppression in radiographic images

ABSTRACT

A method for rib suppression in a chest x-ray image detects and labels one or more ribs in a region of interest and detects rib edges of the one or more detected ribs. Cross rib profiles are generated along the detected ribs. The original x-ray image is conditioned according to at least one of the detected rib edge and cross rib profiles. The conditioned x-ray image can be stored, displayed, or transmitted.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 61/552,658 filed Oct. 28, 2011 in the names of Huo et al.,entitled “RIB SUPPRESSION IN RADIOGRAPHIC IMAGES”, incorporated in itsentirety herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to the field of radiographicimaging and more particularly to methods for detecting and suppressingrib features from a radiographic image, such as the chest.

BACKGROUND

The chest x-ray is useful for detecting a number of patient conditionsand for imaging a range of skeletal and organ structures. Radiographicimages of the chest are useful for detection of lung nodules and otherfeatures that indicate lung cancer and other pathologic structures andother life-threatening conditions. In clinical applications such as inthe Intensive Care Unit (ICU), chest x-rays can have particular valuefor indicating pneumothorax as well as for tube/line positioning, andother clinical conditions. To view the lung fields more clearly andallow more accurate analysis of a patient's condition, it is useful tosuppress the rib cage and related features in the chest x-ray, withoutlosing detail of the lung tissue or other features within the chestcavity.

Methods have been proposed for detecting and suppressing rib structuresand allowing the radiologist to view the lung fields without perceptibleobstruction by the ribs. Some methods have used template matching, ribedge detection, or curve fitting edge detection. Applicants haverecognized that it can be challenging to remove rib features from thechest x-ray image without degrading the underlying image content thatcan include lung tissue.

US Patent Application Publication No. 2009/0290779 entitled“Feature-based neural network regression for feature suppression”(Knapp) describes the use of a trained system for predicting ribcomponents and subsequently subtracting the predicted rib components.

US Patent Application Publication No. 2009/0060366 entitled “Objectsegmentation in images” (Worrell) describes alternative techniques usingdetected rib edges to identify rib structures.

The article entitled “Image-Processing Technique for Suppressing Ribs inChest Radiographs by Means of Massive Training Artificial Neural Network(MTANN)” by Suzuki et al. in IEEE Transactions on Medical Imaging, Vol.25 No. 4, April 2006 describes methods for detection of lung nodules andother features using learned results from a database to optimize ribsuppression for individual patient images.

The article entitled “Detection and Compensation of Rib Structures inChest Radiographs for Diagnose Assistance” in Proceedings of SPIE,3338:774-785 (1998) by Vogelsang et al. describes methods forcompensating for rib structures in a radiographic image. Amongtechniques described in the Vogelsang et al. article are templatematching and generation and selection from candidate parabolas fortracing rib edges.

The article entitled “Model based analysis of chest radiographs”, inProceedings of SPIE 3979, 1040 (2000), also by Vogelsang et al.describes Bezier curve matching to find rib edges in a chest radiographfor alignment of a model and subsequent rib shadow compensation.

While some of these methods may have achieved a level of success usingrib edge detection to identify rib structures that can then besuppressed in the x-ray image, improvements are desired.

Thus, there is a need for a method of rib suppression that accuratelydetects ribs, including clavicles, in chest x-ray images and suppressesthe rib area in a chest x-ray image, while preserving the image contentof underlying lung tissue.

SUMMARY

At least one embodiment of the present invention is directed to ribsuppression in chest x-ray images, while preserving other image content.

Any objects are given only by way of illustrative example, and suchobjects may be exemplary of one or more embodiments of the invention.Other desirable objectives and advantages inherently achieved by thedisclosed invention may occur or become apparent to those skilled in theart. The invention is defined by the appended claims.

According to one aspect of the invention, there is provided a method forrib suppression in a chest x-ray image, the method executed at least inpart by a computer system and comprising: detecting and labeling one ormore ribs in a region of interest in the x-ray image; detecting ribedges of the one or more detected ribs; generating cross rib profilesalong the detected ribs; conditioning the original x-ray image accordingto at least one of the detected rib edge and cross rib profiles; anddisplaying the conditioned x-ray image.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of theinvention will be apparent from the following more particulardescription of the embodiments of the invention, as illustrated in theaccompanying drawings. The elements of the drawings are not necessarilyto scale relative to each other.

FIG. 1 is a logic flow diagram that shows steps of a procedure for ribsuppression according to an embodiment of the present invention.

FIG. 2 is a logic flow diagram that shows processing that is performedin lung segmentation and rib detection.

FIG. 3 is a logic flow diagram that shows iterative processing that isperformed for each detected or labeled rib as part of rib edgesegmentation.

FIG. 4A shows a section of a rib with an identified portion forgenerating a rib profile in a chest x-ray image.

FIG. 4B is a schematic diagram that shows how a cross rib profile for achest x-ray is generated.

FIG. 5 shows an original chest x-ray image prior to processing for ribsuppression.

FIG. 6A shows results from rib detection.

FIG. 6B shows results from rib labeling.

FIG. 7 shows labeled ribs overlaid onto the original image of FIG. 5.

FIGS. 8A and 8B show examples of rib growing algorithms.

FIG. 9 shows a chest x-ray image with suppressed rib content, followinga subtraction operation.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following is a detailed description of the preferred embodiments ofthe invention, reference being made to the drawings in which the samereference numerals identify the same elements of structure in each ofthe several figures.

Applicants have recognized that improvements are desired for methodsusing rib edge detection to identify rib structures that can then besuppressed in the x-ray image. For example, to adapt rib detectionmethods to individual patient images. Methods using template orfunction-fitting of the detected rib edge have limitations forsuccessfully characterizing large variations in the shape of ribs aswell as limitations related to image quality, especially when foreignobjects, e.g., tubes/lines and other devices, are captured in ICUportable chest images. Methods such as the MTANN approach describedabove are not suited to conventional x-ray images, but require dualenergy images as part of the training database. Further, the MTANNtechnique may not be able to accurately estimate the edge of the bone aswell as it estimates bone density elsewhere. In addition, non-zerodensity estimation in non-rib areas can contribute to added noise inthese areas, which affects the overall image quality of the ribsuppressed images. The detection methods described have proved to bememory-intensive, requiring significant computational resources.Robustness is also desirable.

Applicants have recognized a need for a method of rib suppression thatdetects ribs, including clavicles, in chest x-ray images and suppressesthe rib area in a chest x-ray image, meanwhile preserving the imagecontent of underlying lung tissue.

Conventional rib detection techniques typically first locate rib/lineedges, then use rib edge information to identify rib structures that liebetween the rib edges. The inventors have found results from thisconventional approach to be disappointing, often failing to provideaccurate enough information on rib structures for acceptable levels ofrib suppression. Embodiments of the present invention address theproblem of rib suppression in a different manner, by detecting ribregions first, then, once features of individual rib structures havebeen identified, more accurately and robustly locating rib edges. Thisapproach allows the complete rib structure to be identified and itsaffect on image content more accurately profiled than has been achievedusing conventional methods.

The logic flow diagram of FIG. 1 shows a sequence for automated ribsuppression consistent with an embodiment of the present invention forchest x-ray image processing. The chest x-ray image can originate from adigital radiography (DR) detector or from scanned image data. This imagedata may also be obtained from an image archive, such as a PACS (picturearchiving and communication system). In a lung segmentation process 20,the lung and rib cage portions of the image are segmented, thusextracting the lung region of interest from the image. A number ofapproaches to lung segmentation have been proposed, including, forexample, that described in U.S. Pat. No. 7,085,407 entitled “Detectionof Ribcage Boundary from Digital Chest Image” to Ozaki that employslandmark detection and other utilities to detect the boundaries of therib cage. Other methods for lung detection and segmentation includemethods that detect the spine structure and use a bounding box forcoarse detection, with subsequent processing for more closelyidentifying the boundaries of the lung or rib cage. Neural network-basedlogic can also be employed for generating a pixel-based lungsegmentation. Boundary smoothing can also be employed, such as by usingmorphological filtering or other suitable processing technique, forexample.

Continuing with FIG. 1 processing, with the lung region of interest orarea including the lungs identified, a rib detection process 30 follows,in which structural information about the rib features is used inconjunction with image pixel intensities to separate likely rib contentfrom non-rib image content. This step helps to eliminate from processingthe image content that is not obstructed by rib features and has beenfound to provide improved results. Further processing of the candidaterib content is executed in a rib labeling step 40 that groups andorganizes the detected rib contents. In rib labeling step 40,classification of the rib content groups likely rib pixels intocorresponding categories for labeling as part of individual ribs, labelsthese pixels as part of the rib content of the image, and helps toremove false positives from rib detection process 30. Position, shapeinformation, and gradient are used, for example, to help eliminate falsepositives. Processing in step 40 provides for classifying pixels intoone or more of multiple ribs, by using some amount of prior knowledge ofrib structures, such as shape, position, and general direction, and byapplying morphological filtering. Among features that have been found tobe particularly useful for rib classification are rib width andposition, including percentage of pixels initially determined to be partof a rib feature. Other features could similarly be extracted and usedfor false-positive removal. Rib labeling in labeling step 40 alternatelycalculates a medial axis for one or more ribs to generate a skeletalimage for validating rib detection and for subsequent processingincluding rib modeling for retrieving missing or missed-labeled ribs orportion of ribs. The skeletal image has medial axis information and,optionally, other anatomical data relevant to rib location.

Characteristics such as gradient orientation and shape for the labeledrib content can then be used for subsequent processing in a rib edgesegmentation step 50. In rib edge segmentation step 50, edge portions ofthe ribs are identified, and this identification is refined usingiterative processing. Guided growth processing may alternately be usedto enhance rib edge detection. A cross rib profiling step 56 generates across rib profile that provides values for rib compensation along thedetected ribs. Finally, a rib subtraction step 80 is executed,subtracting rib edges and values from the rib profile from the chestx-ray image, to condition the image and provide a rib-suppressed x-rayimage as the conditioned image for display. Other types of conditioningcan be used for combining the detected rib information with the originalx-ray image to generate a rib-suppressed image for display or forfurther analysis.

The logic flow diagram of FIG. 2 shows processing that is performed inlung segmentation process 20, rib detection process 30, and labelingprocess 40, and shows how the results of this processing are used. In anoptional scaling step 22, the image can be scaled to a lower resolutionin order to speed subsequent processing. An extract ROI step 24 helps togenerate position features information for more accurate definition ofthe region of interest (ROI). An image normalization step 26 thenprovides normalized information on image features, consistent withmultiple images.

Rib detection process 30 determines, for pixels in the region ofinterest, whether or not each pixel corresponds to a rib feature. Ribdetection process 30 has a features computation step 28 that computesfeatures for each pixel, such as providing Gaussian derivative featuresinformation and position information, for example. Next, as part of ribdetection step 30, a pixel classification step 32 determines whethereach pixel within the lung region is a rib or non-rib pixel. Classifiertechniques such as artificial neural network, supporting vector machineor random forests that are well known in the art can be used to performthe pixel classification.

In this sequence, labeling process 40 is also shown in more detail. Afalse positive removal step 42 executes for identifying individual ribs.False-positive pixels are first removed as part of this processing. Asubsequent grouping step 36 then determines whether or not one or moregroups of detected pixels can themselves be grouped together as oneindividual rib, based on factors such as positional relationship,connectedness and adjacence, gradient features, and the positionrelative to the central axis of individual groups. These ribs can belabeled according to rib pattern. Global rib modeling, based on ribsthat have already been labeled and known anatomical relationships, canbe used to detect a missing rib from the previous steps.

The logic flow diagram of FIG. 3 shows iterative processing that isperformed for each detected or labeled rib, after the processingdescribed with respect to FIG. 2, as part of rib edge segmentation 50(FIG. 1). The input to this processing is the set of labeled ribs. Amedial axis extraction step 52 obtains the medial axis of each rib. Aninitial smoothing step 58 performs any necessary fitting to smooth ribedges, according to the extracted medial axis. As part of smoothing step58, the smoothed boundaries provide a starting point for more closelyapproximating rib edges. Using the smoothed rib contour, one or moreline segments for the upper or lower rib boundaries are generated asinitial rib edge candidates. Next, one or more additional line segmentcandidates for each segment are generated based on calculated gradientsor other features. A set of the best-fit edge candidates for the upperand lower rib edge is selected, using optimization of a model based onfactors such as edge gradients, rib width, line segment smoothness, andrib shape constraints.

Continuing with the sequence of FIG. 3, a rib growing step 64 continuesthe line segment optimization process of modeling step 60 to extendexisting line segments and merging disconnected line segments as theyare detected or extrapolated from existing segments. A growing algorithmis useful where segments of the ribs are foreshortened or missing. Aspart of the growing algorithm, existing segments are aligned accordingto an anatomy model. Segments are iteratively extended and tested todetermine whether or not growth is completed. Segment growth can alsouse edge extension techniques such as those employed for tubingdetection and described in commonly assigned, copending U.S. PatentApplication No. 2009/0190818 entitled “Computer-Aided Tubing Detection”by Huo.

Repeated iteration of the sequence of steps 58, 60, and 64, as manytimes as needed, helps to improve the collected rib profiles that aregenerated and provided in a cross-rib profile generation step 86, sothat rib data that is combined with the image data in image conditioningstep 92 more accurately characterizes the rib content.

FIGS. 4A and 4B show how a cross rib profile is generated and itsrelationship to the chest x-ray image. In FIG. 4A, a line 74 shows thebasic direction over which the profile is obtained, across the rib in across-sectional manner. In FIG. 4B, a rib 70 is shown schematically incross section, representing a bony shell and a soft interior portion. Aprofile 72 shows how rib 70 affects image data, with peak values alongthe edges. X-rays are generally incident in the direction indicated V inthis figure.

Profile 72 is generated using known characteristics of the rib in thechest x-ray. One method for providing rib profile 72 is to apply alow-pass filter (LPF) to the chest image and use the results of thisprocessing to provide a cross rib profile, which is known to thoseskilled in image processing and analysis. An alternate method employs amodel to provide an initial approximation or starting point fordeveloping the rib profile. Using information from the model alsoenables rib profile information to be identified and extracted from theimage itself. Whatever method is used, the usefulness of the rib profiledepends, in large part, upon accurate detection of rib edges.

The two Vogelsang et al. references cited earlier describe how the crossrib profile can be generated and used. In the article “Model basedanalysis of chest radiographs”, Vogelsang et al. particularly describehow the cross rib profile is used as a model, and show how six regionsfor vertical compensation values are identified and interpolationapplied using this model.

By way of example, FIG. 5 and following show results of some of thesteps of the processing sequence for rib removal according to anembodiment of the present invention. FIG. 5 shows an original chestx-ray image 38 that requires identification and removal of ribs in orderto make underlying tissue more visible. FIG. 6A shows an image 44 thatshows rib detection. FIG. 6B shows an image 46 following rib labelingthat helps to more precisely identify the rib regions. In FIG. 7, animage 62 shows labeled ribs overlaid onto the original image 38.

FIGS. 8A and 8B show an example of rib growing using the overlaidresults of FIG. 7. Rib growing algorithms are of particular value forextending the rib curvature along the ends of the rib, where featuresmay be unclear, and help to provide improved edge detection. In anembodiment of the present invention, rib growing algorithms follow thegeneral curvature of a medial axis 94. FIG. 8B shows an example medialaxis 94 in dashed line form.

Subtraction or other ways of combining rib edge information with thefinal image provide a rib suppressed image, as shown in FIG. 9.

Embodiments of the present invention help to provide more accuratedetection of rib edges than available using conventional methods, suchas shape modeling. In an alternate embodiment of the present invention,only the rib edge profiles are subtracted from the original image toprovide rib suppression.

Consistent with one embodiment, the present invention utilizes acomputer program with stored instructions that perform on image datathat is accessed from an electronic memory. As can be appreciated bythose skilled in the image processing arts, a computer program of anembodiment of the present invention can be utilized by a suitable,general-purpose computer system, such as a personal computer orworkstation. However, many other types of computer systems can be usedto execute the computer program of the present invention, including anarrangement of networked processors, for example. The computer programfor performing the method of the present invention may be stored in acomputer readable storage medium. This medium may comprise, for example;magnetic storage media such as a magnetic disk such as a hard drive orremovable device or magnetic tape; optical storage media such as anoptical disc, optical tape, or machine readable optical encoding; solidstate electronic storage devices such as random access memory (RAM), orread only memory (ROM); or any other physical device or medium employedto store a computer program. The computer program for performing themethod of the present invention may also be stored on computer readablestorage medium that is connected to the image processor by way of theinternet or other network or communication medium. Those skilled in theart will further readily recognize that the equivalent of such acomputer program product may also be constructed in hardware.

It is noted that the term “memory”, equivalent to “computer-accessiblememory” in the context of the present disclosure, can refer to any typeof temporary or more enduring data storage workspace used for storingand operating upon image data and accessible to a computer system,including a database. The memory could be non-volatile, using, forexample, a long-term storage medium such as magnetic or optical storage.Alternately, the memory could be of a more volatile nature, using anelectronic circuit, such as random-access memory (RAM) that is used as atemporary buffer or workspace by a microprocessor or other control logicprocessor device. Display data, for example, is typically stored in atemporary storage buffer that is directly associated with a displaydevice and is periodically refreshed as needed in order to providedisplayed data. This temporary storage buffer can also be considered tobe a memory, as the term is used in the present disclosure. Memory isalso used as the data workspace for executing and storing intermediateand final results of calculations and other processing.Computer-accessible memory can be volatile, non-volatile, or a hybridcombination of volatile and non-volatile types.

It is understood that the computer program product of the presentinvention may make use of various image manipulation algorithms andprocesses that are well known. It will be further understood that thecomputer program product embodiment of the present invention may embodyalgorithms and processes not specifically shown or described herein thatare useful for implementation. Such algorithms and processes may includeconventional utilities that are within the ordinary skill of the imageprocessing arts. Additional aspects of such algorithms and systems, andhardware and/or software for producing and otherwise processing theimages or co-operating with the computer program product of the presentinvention, are not specifically shown or described herein and may beselected from such algorithms, systems, hardware, components andelements known in the art.

It is noted that there can be any of a number of methods used forfunctions such as segmentation of ribs from other tissue in the chestx-ray image or for filtering portions of the image content.

The invention has been described in detail with particular reference toa presently preferred embodiment, but it will be understood thatvariations and modifications can be effected within the spirit and scopeof the invention. The presently disclosed embodiments are thereforeconsidered in all respects to be illustrative and not restrictive. Thescope of the invention is indicated by the appended claims, and allchanges that come within the meaning and range of equivalents thereofare intended to be embraced therein.

What is claimed is:
 1. A method for rib suppression in a chest x-ray image, comprising: detecting and labeling one or more ribs in a region of interest in the x-ray image; detecting rib edges of the one or more detected ribs; generating cross rib profiles along the detected ribs; conditioning the original x-ray image according to at least one of the detected rib edge and cross rib profiles; and storing, displaying, or transmitting the conditioned x-ray image.
 2. The method of claim 1 wherein detecting and labeling the one or more ribs comprises segmenting the lung region of interest from the image.
 3. The method of claim 1 wherein detecting and labeling one or more ribs further comprises extracting a medial axis from at least one of the one or more ribs.
 4. The method of claim 1 further comprising adjusting segments of one or more of the detected rib edges to improve edge fitting.
 5. The method of claim 1 further comprising scaling the image to a reduced resolution.
 6. The method of claim 1 wherein detecting and labeling one or more ribs in the image comprises classifying pixels within the lung region of interest as rib or non-rib pixels.
 7. The method of claim 1 wherein detecting the rib edges further comprises applying a model.
 8. The method of claim 4 wherein adjusting segments comprises applying a growing or extending algorithm for rib edges.
 9. The method of claim 3 further comprising obtaining a skeletal image comprising one or more of the medial axes.
 10. The method of claim 3 further comprising rib modeling.
 11. A method for rib suppression in a chest x-ray image, comprising: segmenting one or more lung regions in the chest image; detecting and labeling one or more rib features within the one or more segmented lung regions; extracting a medial axis from at least one of the one or more detected rib features and iteratively segmenting one or more rib edges from the at least one rib feature according to the extracted medial axis and according to an anatomical model; forming a conditioned image by combining the one or more segmented rib edges with the original x-ray image; and storing, displaying, or transmitting the conditioned image.
 12. The method of claim 11 further comprising scaling the image to a reduced resolution.
 13. The method of claim 11 wherein detecting and labeling one or more rib features in the image comprises classification of pixels within the lung region of interest as rib or non-rib pixels.
 14. The method of claim 11 further comprising extending the one or more segmented rib edges by applying a growing algorithm.
 15. The method of claim 11 wherein forming the conditioned image further comprises applying a filter to the image data.
 16. The method of claim 11 wherein detecting and labeling the one or more rib features further comprises grouping adjacent pixels associated with particular rib features.
 17. A method for rib detection in a chest x-ray image, executed at least in part by a computer system, comprising: detecting pixels for one or more ribs in a region of interest; labeling one or more of the detected ribs as individual ribs by grouping the pixels to generate detected ribs and removing one or more false positive pixels from the detected pixels; detecting rib edge of detected ribs by adjusting one or more edge segments; and storing, displaying, or transmitting the detected results.
 18. The method of claim 17 wherein detecting rib edges further comprises detecting at least one medial axis for each rib and further comprises curve fitting.
 19. The method of claim 17 wherein detecting rib edges further comprises optimizing one or more line segments.
 20. The method of claim 17 further comprising enhancing rib edges by applying a growth algorithm. 